mlsanityData to 14 Oct 2026

Zero-Shot Co-salient Object Detection Framework

arXiv 2309.05499 ↗First posted 11 Sep 2023cs.CVcited by 4

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Abstract (arXiv)

Co-salient Object Detection (CoSOD) endeavors to replicate the human visual system's capacity to recognize common and salient objects within a collection of images. Despite recent advancements in deep learning models, these models still rely on training with well-annotated CoSOD datasets. The exploration of training-free zero-shot CoSOD frameworks has been limited. In this paper, taking inspiration from the zero-shot transfer capabilities of foundational computer vision models, we introduce the first zero-shot CoSOD framework that harnesses these models without any training process. To achieve this, we introduce two novel components in our proposed framework: the group prompt generation (GPG) module and the co-saliency map generation (CMP) module. We evaluate the framework's performance on widely-used datasets and observe impressive results. Our approach surpasses existing unsupervised methods and even outperforms fully supervised methods developed before 2020, while remaining competitive with some fully supervised methods developed before 2022.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 18 of its 29 references that are in the map. First posted on arXiv 11 Sep 2023.

Builds on

18 of 29 references.

References · 18

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2023Segment Anythingcited by 8,234
  3. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  4. 2023Unleashing Text-to-Image Diffusion Models for Visual Perceptioncited by 269
  5. 2020Re-thinking Co-Salient Object Detectioncited by 54
  6. 2022A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detectioncited by 51
  7. 2021Warp Consistency for Unsupervised Learning of Dense Correspondencescited by 42
  8. 2020Gradient-Induced Co-Saliency Detectioncited by 39
  9. 2020Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detectioncited by 35
  10. 2020CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detectioncited by 30
  11. 2021Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detectioncited by 24
  12. 2022Global-and-Local Collaborative Learning for Co-Salient Object Detectioncited by 24
6 more
  1. 2022Texture-guided Saliency Distilling for Unsupervised Salient Object Detectioncited by 24
  2. 2017Group-wise Deep Co-saliency Detectioncited by 15
  3. 2023Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detectioncited by 12
  4. 2022GCoNet+: A Stronger Group Collaborative Co-Salient Object Detectorcited by 11
  5. 2023Co-Salient Object Detection with Co-Representation Purificationcited by 9
  6. 2022Towards Stable Co-saliency Detection and Object Co-segmentationcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 0; 2026: 1.

4 papers.

  1. 2024ASAM: Boosting Segment Anything Model with Adversarial Tuningcited by 12
  2. 2024Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniquescited by 2
  3. 2024Self-supervised co-salient object detection via feature correspondence at multiple scalescited by 1
  4. 2026TF-SSD: A Strong Pipeline via Synergic Mask Filter for Training-free Co-salient Object Detectioncited by 1